Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.

published:19 Jan 2014

views:106060

Introduction to the EM algorithm for maximum likelihood estimation (MLE). EM is particularly applicable when there is "missing data" and one is using an exponential family model. This includes many latent variable models such as mixture models.

published:11 Jul 2011

views:129602

This is, what I hope, a low-math oriented introduction to the EM algorithm. The example I use is from a coin toss, but can be generalized to any example that uses two treatments in which there is data for success or failure. In coin toss terms: two coins and each yields a different average number of heads.

The Forward-Backward algorithm for a hidden Markov model (HMM). How the Forward algorithm and Backward algorithm work together. Discussion of applications (inference, parameter estimation, sampling from the posterior, etc.).

published:07 Jul 2011

views:88273

published:09 Feb 2017

views:2154

published:26 Nov 2013

views:4224

Our algorithm involves both bubble sort and linear search.. the bubble sort was used to put the values in an increasing order, and then we applied linear search... We tried putting it in the concept of making a resistor.. the increasing order is based on the resistor color code table. We are then assigned with a specific resistor that we must make using the colors available :) Have fun and we hope our video can add spice to your curiosity on algorithm.
Cast : Windell Misa, Ericson Ong, Karen Tan,Denzel Wong,
Special thanks to : Jason DyPerez (for taking the video)

An algorithm is an effective method that can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function. Starting from an initial state and initial input (perhaps empty), the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states, eventually producing "output" and terminating at a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate random input.

Expectation–maximization algorithm

In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.

History

The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. They pointed out that the method had been "proposed many times in special circumstances" by earlier authors. In particular, a very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers following his collaboration with Per Martin-Löf and Anders Martin-Löf.
The Dempster-Laird-Rubin paper in 1977 generalized the method and sketched a convergence analysis for a wider class of problems. Regardless of earlier inventions, the innovative Dempster-Laird-Rubin paper in the Journal of the Royal Statistical Society received an enthusiastic discussion at the Royal Statistical Society meeting with Sundberg calling the paper "brilliant". The Dempster-Laird-Rubin paper established the EM method as an important tool of statistical analysis.

EM algorithm: how it works

Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.

14:37

(ML 16.3) Expectation-Maximization (EM) algorithm

(ML 16.3) Expectation-Maximization (EM) algorithm

(ML 16.3) Expectation-Maximization (EM) algorithm

Introduction to the EM algorithm for maximum likelihood estimation (MLE). EM is particularly applicable when there is "missing data" and one is using an exponential family model. This includes many latent variable models such as mixture models.

28:40

EM Algorithm

EM Algorithm

EM Algorithm

This is, what I hope, a low-math oriented introduction to the EM algorithm. The example I use is from a coin toss, but can be generalized to any example that uses two treatments in which there is data for success or failure. In coin toss terms: two coins and each yields a different average number of heads.

EM.3: Visualizing the EM algorithm

14:56

(ML 14.6) Forward-Backward algorithm for HMMs

(ML 14.6) Forward-Backward algorithm for HMMs

(ML 14.6) Forward-Backward algorithm for HMMs

The Forward-Backward algorithm for a hidden Markov model (HMM). How the Forward algorithm and Backward algorithm work together. Discussion of applications (inference, parameter estimation, sampling from the posterior, etc.).

Creative algorithm : DISCRETE MATH -EM

Our algorithm involves both bubble sort and linear search.. the bubble sort was used to put the values in an increasing order, and then we applied linear search... We tried putting it in the concept of making a resistor.. the increasing order is based on the resistor color code table. We are then assigned with a specific resistor that we must make using the colors available :) Have fun and we hope our video can add spice to your curiosity on algorithm.
Cast : Windell Misa, Ericson Ong, Karen Tan,Denzel Wong,
Special thanks to : Jason DyPerez (for taking the video)

EM algorithm: how it works

Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.

published: 19 Jan 2014

(ML 16.3) Expectation-Maximization (EM) algorithm

Introduction to the EM algorithm for maximum likelihood estimation (MLE). EM is particularly applicable when there is "missing data" and one is using an exponential family model. This includes many latent variable models such as mixture models.

published: 11 Jul 2011

EM Algorithm

This is, what I hope, a low-math oriented introduction to the EM algorithm. The example I use is from a coin toss, but can be generalized to any example that uses two treatments in which there is data for success or failure. In coin toss terms: two coins and each yields a different average number of heads.

Mod-04 Lec-10 Mixture Densities, ML estimation and EM algorithm

EM.3: Visualizing the EM algorithm

published: 15 Sep 2015

(ML 14.6) Forward-Backward algorithm for HMMs

The Forward-Backward algorithm for a hidden Markov model (HMM). How the Forward algorithm and Backward algorithm work together. Discussion of applications (inference, parameter estimation, sampling from the posterior, etc.).

published: 07 Jul 2011

Algorithm, The Hacker Movie (Legendado em POR) #JNYTB

Creative algorithm : DISCRETE MATH -EM

Our algorithm involves both bubble sort and linear search.. the bubble sort was used to put the values in an increasing order, and then we applied linear search... We tried putting it in the concept of making a resistor.. the increasing order is based on the resistor color code table. We are then assigned with a specific resistor that we must make using the colors available :) Have fun and we hope our video can add spice to your curiosity on algorithm.
Cast : Windell Misa, Ericson Ong, Karen Tan,Denzel Wong,
Special thanks to : Jason DyPerez (for taking the video)

EM algorithm: how it works

Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sourc...

Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.

Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.

(ML 16.3) Expectation-Maximization (EM) algorithm

Introduction to the EM algorithm for maximum likelihood estimation (MLE). EM is particularly applicable when there is "missing data" and one is using an exponen...

Introduction to the EM algorithm for maximum likelihood estimation (MLE). EM is particularly applicable when there is "missing data" and one is using an exponential family model. This includes many latent variable models such as mixture models.

Introduction to the EM algorithm for maximum likelihood estimation (MLE). EM is particularly applicable when there is "missing data" and one is using an exponential family model. This includes many latent variable models such as mixture models.

EM Algorithm

This is, what I hope, a low-math oriented introduction to the EM algorithm. The example I use is from a coin toss, but can be generalized to any example that us...

This is, what I hope, a low-math oriented introduction to the EM algorithm. The example I use is from a coin toss, but can be generalized to any example that uses two treatments in which there is data for success or failure. In coin toss terms: two coins and each yields a different average number of heads.

This is, what I hope, a low-math oriented introduction to the EM algorithm. The example I use is from a coin toss, but can be generalized to any example that uses two treatments in which there is data for success or failure. In coin toss terms: two coins and each yields a different average number of heads.

The Forward-Backward algorithm for a hidden Markov model (HMM). How the Forward algorithm and Backward algorithm work together. Discussion of applications (inference, parameter estimation, sampling from the posterior, etc.).

The Forward-Backward algorithm for a hidden Markov model (HMM). How the Forward algorithm and Backward algorithm work together. Discussion of applications (inference, parameter estimation, sampling from the posterior, etc.).

Creative algorithm : DISCRETE MATH -EM

Our algorithm involves both bubble sort and linear search.. the bubble sort was used to put the values in an increasing order, and then we applied linear search...

Our algorithm involves both bubble sort and linear search.. the bubble sort was used to put the values in an increasing order, and then we applied linear search... We tried putting it in the concept of making a resistor.. the increasing order is based on the resistor color code table. We are then assigned with a specific resistor that we must make using the colors available :) Have fun and we hope our video can add spice to your curiosity on algorithm.
Cast : Windell Misa, Ericson Ong, Karen Tan,Denzel Wong,
Special thanks to : Jason DyPerez (for taking the video)

Our algorithm involves both bubble sort and linear search.. the bubble sort was used to put the values in an increasing order, and then we applied linear search... We tried putting it in the concept of making a resistor.. the increasing order is based on the resistor color code table. We are then assigned with a specific resistor that we must make using the colors available :) Have fun and we hope our video can add spice to your curiosity on algorithm.
Cast : Windell Misa, Ericson Ong, Karen Tan,Denzel Wong,
Special thanks to : Jason DyPerez (for taking the video)

EM Algorithm

This is, what I hope, a low-math oriented introduction to the EM algorithm. The example I use is from a coin toss, but can be generalized to any example that uses two treatments in which there is data for success or failure. In coin toss terms: two coins and each yields a different average number of heads.

Mod-04 Lec-10 Mixture Densities, ML estimation and EM algorithm

Clustering and EM Algorithm (1/4)

"Google's always used machine learning. In all the areas we applied it to, speech recognition, then image understanding, and eventually language understanding, we saw tremendous improvements."
John Giannandrea, then VP of Engineering, Google
In the last decade, machine learning has been applied to many real world problems successfully. It is considered as the most essential and fundmental knowledge for a data scientist. We introduce core concept of machine learning and several useful learning methods including linear models, nonlinear models, kernel methods, dimension reduction, unsupervised learning (Clustering) and deep learning. Also some special topics and applications will be discussed.
授課教師：應用數學系 李育杰老師
課程資訊：http://ocw.nctu.edu.tw/course_detail.php?bgid=1&gid=1&nid=563
授權條款：Creati...

Algorithm, The Hacker Movie (Legendado em POR) #JNYTB

How to: Work at Google — Example Coding/Engineering Interview

Watch our video to see two Google engineers demonstrate a mock interview question. After they code, our engineers highlight best practices for interviewing at Google.
Learn more about how we hire at http://goo.gl/xSD7jo, then head over to https://goo.gl/BEKV6Z to find your role.
Also check out our companion video, How to Work at Google: Prepare for an EngineeringInterview (https://goo.gl/e0i8rX).
Subscribe to Life at Google for more videos → https://goo.gl/kqwUZd
Follow us!
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published: 07 Nov 2016

Clustering and EM Algorithm (2/4)

"Google's always used machine learning. In all the areas we applied it to, speech recognition, then image understanding, and eventually language understanding, we saw tremendous improvements."
John Giannandrea, then VP of Engineering, Google
In the last decade, machine learning has been applied to many real world problems successfully. It is considered as the most essential and fundmental knowledge for a data scientist. We introduce core concept of machine learning and several useful learning methods including linear models, nonlinear models, kernel methods, dimension reduction, unsupervised learning (Clustering) and deep learning. Also some special topics and applications will be discussed.
授課教師：應用數學系 李育杰老師
課程資訊：http://ocw.nctu.edu.tw/course_detail.php?bgid=1&gid=1&nid=563
授權條款：Creati...

This lecture builds on top of the Bayesian classifier that we developed last time. Specifically we build an expectation-maximization (EM) algorithm that locally maximizes the likelihood function. We go over an improved python implementation from the last lecture where we reduce the size of training set dramatically and measure the convergence of EM. This shows that using the testing data for improving the model greatly helps in improving the accuracy of the algorithm.

EM Algorithm

This is, what I hope, a low-math oriented introduction to the EM algorithm. The example I use is from a coin toss, but can be generalized to any example that us...

This is, what I hope, a low-math oriented introduction to the EM algorithm. The example I use is from a coin toss, but can be generalized to any example that uses two treatments in which there is data for success or failure. In coin toss terms: two coins and each yields a different average number of heads.

This is, what I hope, a low-math oriented introduction to the EM algorithm. The example I use is from a coin toss, but can be generalized to any example that uses two treatments in which there is data for success or failure. In coin toss terms: two coins and each yields a different average number of heads.

Clustering and EM Algorithm (1/4)

"Google's always used machine learning. In all the areas we applied it to, speech recognition, then image understanding, and eventually language understanding, ...

"Google's always used machine learning. In all the areas we applied it to, speech recognition, then image understanding, and eventually language understanding, we saw tremendous improvements."
John Giannandrea, then VP of Engineering, Google
In the last decade, machine learning has been applied to many real world problems successfully. It is considered as the most essential and fundmental knowledge for a data scientist. We introduce core concept of machine learning and several useful learning methods including linear models, nonlinear models, kernel methods, dimension reduction, unsupervised learning (Clustering) and deep learning. Also some special topics and applications will be discussed.
授課教師：應用數學系 李育杰老師
課程資訊：http://ocw.nctu.edu.tw/course_detail.php?bgid=1&gid=1&nid=563
授權條款：Creative CommonsBY-NC-SA3.0
更多課程歡迎瀏覽交大開放式課程網站：http://ocw.nctu.edu.tw/

"Google's always used machine learning. In all the areas we applied it to, speech recognition, then image understanding, and eventually language understanding, we saw tremendous improvements."
John Giannandrea, then VP of Engineering, Google
In the last decade, machine learning has been applied to many real world problems successfully. It is considered as the most essential and fundmental knowledge for a data scientist. We introduce core concept of machine learning and several useful learning methods including linear models, nonlinear models, kernel methods, dimension reduction, unsupervised learning (Clustering) and deep learning. Also some special topics and applications will be discussed.
授課教師：應用數學系 李育杰老師
課程資訊：http://ocw.nctu.edu.tw/course_detail.php?bgid=1&gid=1&nid=563
授權條款：Creative CommonsBY-NC-SA3.0
更多課程歡迎瀏覽交大開放式課程網站：http://ocw.nctu.edu.tw/

How to: Work at Google — Example Coding/Engineering Interview

Watch our video to see two Google engineers demonstrate a mock interview question. After they code, our engineers highlight best practices for interviewing at G...

Watch our video to see two Google engineers demonstrate a mock interview question. After they code, our engineers highlight best practices for interviewing at Google.
Learn more about how we hire at http://goo.gl/xSD7jo, then head over to https://goo.gl/BEKV6Z to find your role.
Also check out our companion video, How to Work at Google: Prepare for an EngineeringInterview (https://goo.gl/e0i8rX).
Subscribe to Life at Google for more videos → https://goo.gl/kqwUZd
Follow us!
Twitter: https://goo.gl/kdYxFP
Facebook: https://goo.gl/hXDzLf
Google Plus: https://goo.gl/YBcMZK

Watch our video to see two Google engineers demonstrate a mock interview question. After they code, our engineers highlight best practices for interviewing at Google.
Learn more about how we hire at http://goo.gl/xSD7jo, then head over to https://goo.gl/BEKV6Z to find your role.
Also check out our companion video, How to Work at Google: Prepare for an EngineeringInterview (https://goo.gl/e0i8rX).
Subscribe to Life at Google for more videos → https://goo.gl/kqwUZd
Follow us!
Twitter: https://goo.gl/kdYxFP
Facebook: https://goo.gl/hXDzLf
Google Plus: https://goo.gl/YBcMZK

Clustering and EM Algorithm (2/4)

"Google's always used machine learning. In all the areas we applied it to, speech recognition, then image understanding, and eventually language understanding, ...

"Google's always used machine learning. In all the areas we applied it to, speech recognition, then image understanding, and eventually language understanding, we saw tremendous improvements."
John Giannandrea, then VP of Engineering, Google
In the last decade, machine learning has been applied to many real world problems successfully. It is considered as the most essential and fundmental knowledge for a data scientist. We introduce core concept of machine learning and several useful learning methods including linear models, nonlinear models, kernel methods, dimension reduction, unsupervised learning (Clustering) and deep learning. Also some special topics and applications will be discussed.
授課教師：應用數學系 李育杰老師
課程資訊：http://ocw.nctu.edu.tw/course_detail.php?bgid=1&gid=1&nid=563
授權條款：Creative CommonsBY-NC-SA3.0
更多課程歡迎瀏覽交大開放式課程網站：http://ocw.nctu.edu.tw/

"Google's always used machine learning. In all the areas we applied it to, speech recognition, then image understanding, and eventually language understanding, we saw tremendous improvements."
John Giannandrea, then VP of Engineering, Google
In the last decade, machine learning has been applied to many real world problems successfully. It is considered as the most essential and fundmental knowledge for a data scientist. We introduce core concept of machine learning and several useful learning methods including linear models, nonlinear models, kernel methods, dimension reduction, unsupervised learning (Clustering) and deep learning. Also some special topics and applications will be discussed.
授課教師：應用數學系 李育杰老師
課程資訊：http://ocw.nctu.edu.tw/course_detail.php?bgid=1&gid=1&nid=563
授權條款：Creative CommonsBY-NC-SA3.0
更多課程歡迎瀏覽交大開放式課程網站：http://ocw.nctu.edu.tw/

This lecture builds on top of the Bayesian classifier that we developed last time. Specifically we build an expectation-maximization (EM) algorithm that locally maximizes the likelihood function. We go over an improved python implementation from the last lecture where we reduce the size of training set dramatically and measure the convergence of EM. This shows that using the testing data for improving the model greatly helps in improving the accuracy of the algorithm.

This lecture builds on top of the Bayesian classifier that we developed last time. Specifically we build an expectation-maximization (EM) algorithm that locally maximizes the likelihood function. We go over an improved python implementation from the last lecture where we reduce the size of training set dramatically and measure the convergence of EM. This shows that using the testing data for improving the model greatly helps in improving the accuracy of the algorithm.

EM algorithm: how it works

Full lecture: http://bit.ly/EM-alg
Mixture models are a probabilistically-sound way to do soft clustering. We assume our data is sampled from K different sources (probability distributions). The expectation maximisation (EM) algorithm allows us to discover the parameters of these distributions, and figure out which point comes from each source at the same time.

14:37

(ML 16.3) Expectation-Maximization (EM) algorithm

Introduction to the EM algorithm for maximum likelihood estimation (MLE). EM is particular...

(ML 16.3) Expectation-Maximization (EM) algorithm

Introduction to the EM algorithm for maximum likelihood estimation (MLE). EM is particularly applicable when there is "missing data" and one is using an exponential family model. This includes many latent variable models such as mixture models.

28:40

EM Algorithm

This is, what I hope, a low-math oriented introduction to the EM algorithm. The example I ...

EM Algorithm

This is, what I hope, a low-math oriented introduction to the EM algorithm. The example I use is from a coin toss, but can be generalized to any example that uses two treatments in which there is data for success or failure. In coin toss terms: two coins and each yields a different average number of heads.

(ML 14.6) Forward-Backward algorithm for HMMs

The Forward-Backward algorithm for a hidden Markov model (HMM). How the Forward algorithm and Backward algorithm work together. Discussion of applications (inference, parameter estimation, sampling from the posterior, etc.).

Creative algorithm : DISCRETE MATH -EM

Our algorithm involves both bubble sort and linear search.. the bubble sort was used to put the values in an increasing order, and then we applied linear search... We tried putting it in the concept of making a resistor.. the increasing order is based on the resistor color code table. We are then assigned with a specific resistor that we must make using the colors available :) Have fun and we hope our video can add spice to your curiosity on algorithm.
Cast : Windell Misa, Ericson Ong, Karen Tan,Denzel Wong,
Special thanks to : Jason DyPerez (for taking the video)

58:17

Algorithm EM 10 webinar: Core ERP

In our August 8th webinar we covered the Distribution, Manufacturing and Quality component...

EM Algorithm

This is, what I hope, a low-math oriented introduction to the EM algorithm. The example I use is from a coin toss, but can be generalized to any example that uses two treatments in which there is data for success or failure. In coin toss terms: two coins and each yields a different average number of heads.

Clustering and EM Algorithm (1/4)

"Google's always used machine learning. In all the areas we applied it to, speech recognition, then image understanding, and eventually language understanding, we saw tremendous improvements."
John Giannandrea, then VP of Engineering, Google
In the last decade, machine learning has been applied to many real world problems successfully. It is considered as the most essential and fundmental knowledge for a data scientist. We introduce core concept of machine learning and several useful learning methods including linear models, nonlinear models, kernel methods, dimension reduction, unsupervised learning (Clustering) and deep learning. Also some special topics and applications will be discussed.
授課教師：應用數學系 李育杰老師
課程資訊：http://ocw.nctu.edu.tw/course_detail.php?bgid=1&gid=1&nid=563
授權條款：Creative CommonsBY-NC-SA3.0
更多課程歡迎瀏覽交大開放式課程網站：http://ocw.nctu.edu.tw/

How to: Work at Google — Example Coding/Engineering Interview

Watch our video to see two Google engineers demonstrate a mock interview question. After they code, our engineers highlight best practices for interviewing at Google.
Learn more about how we hire at http://goo.gl/xSD7jo, then head over to https://goo.gl/BEKV6Z to find your role.
Also check out our companion video, How to Work at Google: Prepare for an EngineeringInterview (https://goo.gl/e0i8rX).
Subscribe to Life at Google for more videos → https://goo.gl/kqwUZd
Follow us!
Twitter: https://goo.gl/kdYxFP
Facebook: https://goo.gl/hXDzLf
Google Plus: https://goo.gl/YBcMZK

25:58

Clustering and EM Algorithm (2/4)

"Google's always used machine learning. In all the areas we applied it to, speech recognit...

Clustering and EM Algorithm (2/4)

"Google's always used machine learning. In all the areas we applied it to, speech recognition, then image understanding, and eventually language understanding, we saw tremendous improvements."
John Giannandrea, then VP of Engineering, Google
In the last decade, machine learning has been applied to many real world problems successfully. It is considered as the most essential and fundmental knowledge for a data scientist. We introduce core concept of machine learning and several useful learning methods including linear models, nonlinear models, kernel methods, dimension reduction, unsupervised learning (Clustering) and deep learning. Also some special topics and applications will be discussed.
授課教師：應用數學系 李育杰老師
課程資訊：http://ocw.nctu.edu.tw/course_detail.php?bgid=1&gid=1&nid=563
授權條款：Creative CommonsBY-NC-SA3.0
更多課程歡迎瀏覽交大開放式課程網站：http://ocw.nctu.edu.tw/

This lecture builds on top of the Bayesian classifier that we developed last time. Specifically we build an expectation-maximization (EM) algorithm that locally maximizes the likelihood function. We go over an improved python implementation from the last lecture where we reduce the size of training set dramatically and measure the convergence of EM. This shows that using the testing data for improving the model greatly helps in improving the accuracy of the algorithm.

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It turns out that a theory explaining how we might detect parallel universes and prediction for the end of the world was proposed and completed by physicist Stephen Hawking shortly before he died ... &nbsp;. According to reports, the work predicts that the universe would eventually end when stars run out of energy ... ....

Article by WN.Com Correspondent Dallas DarlingIt wasn’t very long ago Republicans were accusing Democrats of either paying a few dollars to the homeless for votes or giving them a pack of cigarettes. But with Donald Trump, it’s obvious he paid $130,000 to an adult-film star in exchange for her silence last October and just before the general election ... Was the payment from his own account – or from a lawyer – or from campaign donations....

Using e-cigarettes may lead to an accumulation of fat in the liver, a study of mice exposed to the devices suggests. “The popularity of electronic cigarettes has been rapidly increasing in part because of advertisements that they are safer than conventional cigarettes ... Friedman of Charles R. Drew University of Medicine and Science in Los Angeles, California ... Circadian rhythm dysfunction is known to accelerate liver disease....

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Scientists have developed a novel algorithm that may help detect and prevent cyber attacks on GPS-enabled devices in real time. The algorithm developed by researchers at University of Texas at San Antonio (UTSA) in the US mitigates the effects of spoofed GPS attacks on electrical grids and other GPS-reliant technologies ... The algorithm, which can be ......

Monero lead developer Riccardo Spagni has emphasized that the units will not work on monero due to a scheduled hard fork designed specifically to outwit the Cryptonight algorithm... “We are pleased to announce the all-new Antminer X3, to mine cryptocurrencies based on the Cryptonight hashing algorithm,” tweeted Bitmain cheerily on March 15....

Not only does the developed market have an aging population that is less likely to drive demand for EM products, but the demographics of emerging markets is quite the opposite. EM countries have a growing population of younger, potential workers, that are contributing to an evolution in emerging market production from manufacturing and exports, to imports and consumption ... Even the bond market within EM countries has evolved....

What can technology do for education? More specifically, how can it help minorities and low-income students overcome barriers and achieve their goals? These are questions posed by Algorithm for Change, a new national competition organized by the NYU Social Entrepreneurship Program that will start accepting applications this spring ...Algorithm for Change seeks to fix this disparity....

Em takes no prisoners (Picture. Getty) ... The Detroit rapper has been sparked into action against the powerful US gun lobby in recent weeks, with Em being inspired to get involved following the response to the latest high school shooting tragedy in Florida last month ... Dont hold back, EM – your move, NRA. Got a story? ... MORE ... MORE ... ....

We lead Europe in containing the Russkies, not the Brussels bureaucrats, not the Germans and not the French. And, strange as it may seem, some of the Continentals are admiring us for it ...First we get ‘em interested ... Then we decide what we’ll let ‘em have.” ... ....

The action by the senator and the House members follows the decision by the Justice Department to force RT America to register as a foreign agent and the imposition of algorithms by Facebook, Google and Twitter that steer traffic away from left-wing, anti-war and progressive websites, including Truthdig ... And the situation appears to be growing worse as the algorithms are refined....

Two men were injured in an explosion in Austin and officials are investigating to see if it is connected to earlier bombings in that city ... According to the Austin-Travis CountyEMS, officials are responding Sunday night to an incident on Eagle FeatherDrive ... FINAL ... Two men in their 20s patients were injured and transported to a hospital, where they are expected to survive, EMS officials said in a tweet. UPDATE ... EMBED More News Videos ... ....

Two men were injured in an explosion in Austin and officials are investigating to see if it is connected to earlier bombings in that city ... According to the Austin-Travis CountyEMS, officials are responding Sunday night to an incident on Eagle FeatherDrive ... FINAL ... Two men in their 20s patients were injured and transported to a hospital, where they are expected to survive, EMS officials said in a tweet. UPDATE ... EMBED More News Videos ... ....

“Social MediaRoundup” is a weekly roundup of all the important social media news you need to know about. This week, Facebook announced a new Android app, YouTube rolled out a new Dark Mode, Facebook banned racist Pages and Instagram debunked a rumor about its algorithm... ....